Category: Spark

In the couple of days left before the year end I wanted to look back and reflect on what has happened so far in the IT bubble 2.0 commonly referred to as “BigData”. Here are some of my musings.

Let’s start with this simple statement: BigData is misnomer. Most likely it has been put forward by some PR or MBA schmuck with no imagination whatsoever, who thought that terabyte consists of 1000 megabytes 😉 The word has been picked up by pointy-haired bosses all around the world as they need buzzwords to justify their existence to people around. But I digressed…

So what has happened in the last 12 months in this segment of software development? Well, surprisingly you can count real interesting events on one hand. To name a few:

Fault tolerance in the distributed systems got to the new level with NonStop Hadoop, introduced by WANdisco earlier this year. The idea of avoiding complex screw-ups by agreeing on the operations up-front is leaving things like Linux HA, Hadoop QJM, and NFS based solutions rolling in the dust in the rear-view mirror.

Hadoop HDFS is clearly here to stay: you can see customers shifting from platforms like Teradata towards cheaper and widely supported HDFS network storage; with EMC (VMWare, Greenplum, etc.) offering it as the storage layer under Greenplum’s proprietary PostegSQL cluster and many others.

While enjoying a huge head start, HDFS has a strong while not very obvious competitor – CEPH. As some know, there’s a patch that provides CEPH drop-in replacement for HDFS. But where it get real interesting is how systems like Spark (see next paragraph) can work directly on top of CEPH file-system with a relatively small changes in the code. Just picture it:

distributed Linux file-system high-speed data analytic

Drawing conclusions is left as an exercise to the readers.

With the recent advent and fast rise of new in memory analytic platform – Apache Spark (incubating) – the traditional, two bit, MapReduce paradigm is loosing the grasp very quickly. The gap is getting wider with new generation of the task and resource schedulers gaining momentum by the day: Mesos, Spark standalone scheduler, Sparrow. The latter is especially interesting with its 5ms scheduling guarantees. That leaves the latest reincarnation of the MR in the predicament.

Shark – SQL layer on top of Spark – is winning the day in the BI world, as you can see it gaining more popularity. It seems to have nowhere to go but up, as things like Impala, Tez, ASF Drill are still very far away from being accepted in the data-centers.

With all above it is very exciting to see my good friends from AMPlab spinning up a new company that will be focusing on the core platform of Spark, Shark and all things related. All best wishes to Databricks in the coming year!

Speaking of BI, it is interesting to see that Bigdata BI and BA companies are still trying to prove their business model and make it self-sustainable. The case in point, Datameer with recent $19M D-round; Platfora’s last year $20M B-round, etc. I reckon we’ll see more fund-raisers in the 107 or perhaps 108 of dollars in the coming year among the application companies and platform ones. Also new letters will be added to the mix: F-rounds, G-rounds, etc. as cheap currency keeps finding its way from the Fed through the financial sector to the pockets of VCs and further down to high-risk sectors like IT and software development. This will lead to over-heated job market in the Silicon Valley and elsewhere followed by a blow-up similar to but bigger than 2000-2001. It will be particularly fascinating to watch big companies scavenging the pieces after the explosion. So duck to avoid shrapnel.

Stack integration and validation has became a pain-point for many. And I see the effects of it in shark uptake of the interest and growth of Apache Bigtop community. Which is no surprise, considering that all commercial distributions of Hadoop today are based or directly using Bigtop as the stack producing framework.

While I don’t have a crystal ball (would be handy sometimes) I think a couple of very strong trends are emerging in this segment of the technology:

HDFS availability – and software stack availability in general – is a big deal: with more and more companies adding HDFS layer into their storage stack more strict SLAs will emerge. And I am not talking about 5 nines – an equivalent of 5 minutes downtime per year – but rather about 6 and 7 nines. I think Zookeeper based solutions are in for a rough ride.

Machine Learning has a huge momentum. Spark summit was a one big evidence of it. With this comes the need to incredibly fast scheduling and hardware utilization. Hence things like Mesos, Spark standalone and Sparrow are going to keep gaining the momentum.

Seasonal lemming-like migration to the cloud will continue, I am afraid. The security will become a red-hot issue and an investment opportunity. However, anyone who values their data is unlikely to move to the public cloud, hence – private platforms like OpenStack might be on the rise (if the providers can deal with “design by committee” issues of course).

Storage and analytic stack deployment and orchestration will be more pressing than ever (no, I am talking about real orchestration, not cluster management software). That’s why I am looking very closely on that companies like Reactor8 are doing in this space.

So, last year brought a lot of excitement and interesting challenges. 2014, I am sure, will be even more fun. However “living in the interesting times” might a curse and a blessing. Stay safe, my friends!

I have came across this post from Platfora which, among other trivialities, says:

Hadoop is irresistible for this reason, but the big question that remains is how to use the data there once you’ve stored it. The challenge is that Hadoop is a very different architecture to traditional data warehouses. It is a batch engine — a lumbering freight train that can process immense amounts of data, but takes a while to get up to speed, so even the simplest question requires minutes of processing.

How lyrical! And then we got a glimpse of The Promised Land laying ahead:

Here at Platfora we are laser focused on this next phase of Hadoop. The result won’t just match the status quo, but exceed it in flexibility and the ability to scale and adapt to changing requirements. Exciting times are ahead – stay tuned.

No, wait – not an exactly promised land: just a promise of one. I wonder if this an attempt to damage control of yesterday’s announcement about a vendor’s support for Spark platform, that I was discussing in my last post? 🙂